Large Language Model Enhanced Recommender Systems: A Survey

• URL: http://arxiv.org/abs/2412.13432v3
• Date: Mon, 10 Mar 2025 08:49:00 GMT
• Title: Large Language Model Enhanced Recommender Systems: A Survey
• Authors: Qidong Liu, Xiangyu Zhao, Yuhao Wang, Yejing Wang, Zijian Zhang, Yuqi Sun, Xiang Li, Maolin Wang, Pengyue Jia, Chong Chen, Wei Huang, Feng Tian,
• Abstract summary: This paper presents a survey of the latest research efforts aimed at leveraging Large Language Model (LLM) to enhance recommender systems (RS)<n>We identify a critical shift in the field with the move towards incorporating LLM into the online system, notably by avoiding their use during inference.
• Score: 31.31030891846837
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Model (LLM) has transformative potential in various domains, including recommender systems (RS). There have been a handful of research that focuses on empowering the RS by LLM. However, previous efforts mainly focus on LLM as RS, which may face the challenge of intolerant inference costs by LLM. Recently, the integration of LLM into RS, known as LLM-Enhanced Recommender Systems (LLMERS), has garnered significant interest due to its potential to address latency and memory constraints in real-world applications. This paper presents a comprehensive survey of the latest research efforts aimed at leveraging LLM to enhance RS capabilities. We identify a critical shift in the field with the move towards incorporating LLM into the online system, notably by avoiding their use during inference. Our survey categorizes the existing LLMERS approaches into three primary types based on the component of the RS model being augmented: Knowledge Enhancement, Interaction Enhancement, and Model Enhancement. We provide an in-depth analysis of each category, discussing the methodologies, challenges, and contributions of recent studies. Furthermore, we highlight several promising research directions that could further advance the field of LLMERS.

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